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A Neurocognitive Model of Advertisement Content and Brand Name Recall

Author

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  • Antonio G. Chessa

    (Statistics Netherlands (CBS), P.O. Box 4000, 2270 JM, Voorburg, The Netherlands)

  • Jaap M. J. Murre

    (Department of Psychonomy, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam, The Netherlands)

Abstract

We introduce a new (point process) model of learning and forgetting, inspired by the structures of the brain, that we apply to model long-term memory for advertising and brand name recall. Recall-probability functions derived from the model are tested with classic data by Zielske [Zielske, H. A. 1959. The remembering and forgetting of advertising. 239–243], as well as advertisement content and brand name recall data of a Dutch study that tracked over 40 campaigns of TV commercials. Data fits and cross-validation results indicate that the recall functions serve as a good first approximation for aggregate behavior. The shapes of optimal GRP schedules, which are obtained by maximizing a recall measure, are strongly related to the model parameters and corresponding memory processes. Comparisons with existing models in the literature indicate that a neurobiologically motivated model may give a more realistic description of memory for advertisements.

Suggested Citation

  • Antonio G. Chessa & Jaap M. J. Murre, 2007. "A Neurocognitive Model of Advertisement Content and Brand Name Recall," Marketing Science, INFORMS, vol. 26(1), pages 130-141, 01-02.
    • Handle: RePEc:inm:ormksc:v:26:y:2007:i:1:p:130-141
    DOI: 10.1287/mksc.1060.0212
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    References listed on IDEAS

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    Cited by:

    1. Norris I. Bruce, 2008. "Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters," Marketing Science, INFORMS, vol. 27(4), pages 659-673, 07-08.
    2. Wenjie Tang & Tong Wang & Wenxin Xu, 2022. "Sooner or Later? The Role of Adoption Timing in New Technology Introduction," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1663-1678, April.
    3. Jaap M J Murre & Joeri Dros, 2015. "Replication and Analysis of Ebbinghaus’ Forgetting Curve," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-23, July.
    4. David Godes & Elie Ofek & Miklos Sarvary, 2009. "Content vs. Advertising: The Impact of Competition on Media Firm Strategy," Marketing Science, INFORMS, vol. 28(1), pages 20-35, 01-02.
    5. Yilmaz Kocer, 2010. "Endogenous Learning with Bounded Memory," Working Papers 1290, Princeton University, Department of Economics, Econometric Research Program..
    6. Nobuhiko Terui & Masataka Ban & Greg M. Allenby, 2011. "The Effect of Media Advertising on Brand Consideration and Choice," Marketing Science, INFORMS, vol. 30(1), pages 74-91, 01-02.
    7. Li, Yang & Liu, Feng, 2021. "Joint inventory and pricing control with lagged price responses," International Journal of Production Economics, Elsevier, vol. 241(C).
    8. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    9. Philip Hans Franses, 2021. "Marketing response and temporal aggregation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 111-117, June.

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